chi-square test

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statistics, science of collecting and classifying a group of facts according to their relative number and determining certain values that represent characteristics of the group. The most familiar statistical measure is the arithmetic mean, which is an average value for a group of numerical observations. A second important statistic or statistical measure is the standard deviation, which is a measure of how much the individual observations are scattered about the mean. The chi-square test is a method of determining the odds for or against a given deviation from expected statistical distribution. Other statistics indicate other characteristics of the group of observations. In addition to the problem of computing certain statistics for a particular group of observations, there is the problem of sampling. This is an attempt to determine for what larger group (called the population) of individuals or characteristics the statistics for this particular group (called the sample) would be a representative figure and how representative a figure it would be for a given larger group. This second problem of sampling can be solved only by resorting to the theory of probability and higher mathematics. In most applications of statistics to scientific and social research, insurance, and finance, the statistician is interested not only in the characteristics of the sample but also in those of some much larger population. Consequently, the theory of sampling is the most important part of statistical theory.


See J. F. Freund, Modern Elementary Statistics (1988); D. S. Moore and G. P. McCabe, Introduction to the Practice of Statistics (1989); D. H. Sanders, Statistics (1989).

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chi-square test

[′kī ¦skwer ′test]
A generalization, and an extension, of a test for significant differences between a binomial population and a multinomial population, wherein each observation may fall into one of several classes and furnishes a comparison among several samples instead of just two.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
According to the Pearson chi2 test (0.001), there is a statistically significant association between the patients' pathologies and the presence of pain.
Despite that, 7.2 of AAS abusers were younger than25years old but no significant difference was observed between different age groups and prevalence of AAS use with chi2 test in the present study, but other national studies reported significant difference based on age (4, 12, 22); therefore further studies with larger sample size are required.
Categorical variables were compared in the two areas using Fisher exact test, whereas quantitative variables were compared with Chi2 tests. Logistic regressions were adjusted for main potential confounders: age, sex, education level, smoking, and body mass index.
The difference between groups was evaluated using a Chi2 test. Student's t-test, or ANOVA, whichever was appropriate, with a p-value of less than 0.05 being considered significant.
A univariate and bivariate analysis took place, as well as differences in means and proportions with the student's t and the chi2 test, respectively.
Table 3 The GMM Estimates, Dependent Variable is INF Variables (1) (2) (3) RD -1 175 *** (0.409) ED -2.337 (4.467) ED -1.227 *** (0.427) Tax/GDP -1.395 ** -0.973 -1.514 ** (0.670) (1.104) (0.677) Openness -1.070 *** -1.082 ** -1.023 *** (0.212) (0.540) (0.222) M2/GDP -1.423 -1.403 -1.255 (1.092) (2.054) (1.121) Physical Capital -1.202 *** -0.803 -1.269 *** (0.356) (0.512) (0.367) Constant 29 24 *** 17.76 * 31.39 *** (9.699) (10.22) (10.10) Observations 37 37 37 R-squared 0.628 0.382 0.616 Wald Chi2 Test 98.89 41.37 94.72 Normality Test 1.16(0.56) 2.19(0.33) 1.63 (0.44) End.
In statistics, CHI2 test is applied to examine independence of two events.
For comparisons between the two groups, we applied the Chi2 test of independence test.
There was no significant difference was the percentage of Enterococcus faecalis infections (Pearson chi2 test = 2.9649, p = 0.085).
We will test the impact of loyalty programs on customer loyalty by measuring the percentage of answers, from members and non-member, whose score is equal to or higher than 5 for these five items (on our Likert scale with 7 levels, 4 corresponds to a situation where the consumer is indifferent), then by checking our results with a Chi2 test if the differences obtained are significant.
The purpose of carrying out a Chi2 test is to determine the existence of statistically significant differences and the relation of independence in the foods sector.
In order to establish any association between the level of pain and the drugs administration, was used the Chi2 test of associaton (independency).